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International Symposium on Visual Computing

ISVC 2015: Advances in Visual Computing pp 646–654Cite as

Efficient Training of Evolution-Constructed Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

Abstract

Evolution-Constructed (ECO) features have been shown to be effective for general object recognition. ECO features use evolution strategies to build series of transforms and thus can be generated automatically without human expert involvement. We improved on our successful ECO features algorithm by reducing their dimensions before putting them into the classifier in order to create more effective ECO features. Efficient training of ECO features allows features to be more robust in representing the images.

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Acknowledgement

The project was supported by the Small Business Innovation Research program of the U.S. Department of Agriculture, grant number #2014-33610-21951.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, 84602, USA

    Meng Zhang & Dah-Jye Lee

Authors
  1. Meng Zhang
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  2. Dah-Jye Lee
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Corresponding author

Correspondence to Meng Zhang .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Zhang, M., Lee, DJ. (2015). Efficient Training of Evolution-Constructed Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_60

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_60

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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